Environmental Pollution in North-Central Nigeria: Transport Mechanisms, Multimedia Contaminant Dynamics, and Quantitative Risk Characterization, A Systematic Review of Peer-Reviewed Studies (2010–2024)

Pollution across North-Central Nigeria covering Benue, Kogi, Kwara, Nasarawa, Niger and Plateau States together with the Federal Capital Territory (FCT) is a tangled, multi-source problem. The main drivers are artisanal mining, open waste dumping, urban and industrial emissions, river contamination, and heavy agrochemical use. Most existing assessments stay at the geochemical surface and lean on deterministic methods, which leaves transport mechanisms poorly understood and rarely accounts for probabilistic uncertainty. Following PRISMA 2020 (protocol on the Open Science Framework; DOI:), we reviewed 48 peer-reviewed studies published between 2010 and 2024. Records were drawn from Scopus, Web of Science, ScienceDirect, SpringerLink, MDPI and AJOL between 1 January and 31 March 2024. Two reviewers screened independently (Cohen's κ = 0.84), and methodological quality was scored on a modified Newcastle–Ottawa Scale. Reported contamination was interpreted through established environmental physics: advection–dispersion, Stokes' law, Darcy Richards flow, sorption thermodynamics, gas–particle partitioning, and atmospheric boundary-layer dynamics. Deterministic risk indices (CF, I_geo, EF, PLI, PERI, HQ, HI and ILCR) were folded into a single exposure-pathway model. A Monte Carlo simulation (10,000 iterations; Python, NumPy v1.24) propagated uncertainty across pathway-specific hazard estimates using parameter distributions taken directly from the included studies. Heavy metals dominated 46 of the 48 studies (95.8%). Mining sediments followed the order Fe (median 15,200 mg kg⁻¹; IQR 8,400–22,000) > Cr (320; 180–560) > As (45; 18–95) > Zn (180; 95–340) > Pb (95; 42–180) > Cd (8.5; 3.2–18). PM₂.₅ (15–142 µg m⁻³) exceeded the WHO guideline of 5 µg m⁻³ in every one of the nine air-quality studies from Abuja and Ilorin, and mineral dust supplied up to 40.5% of the fine-particle mass. Organochlorine pesticides endosulfan and lindane mainly lingered in farmland soils at 0.15–2.3 µg kg⁻¹ in 6 of 7 agricultural studies. The children's median HI was 2.8 times that of adults (IQR 1.9–4.2; n = 44). Monte Carlo outputs gave P(HI > 1) = 0.94 (95% CI: 0.91–0.97) for high-exposure mining scenarios. Concentration variability (Spearman ρ = 0.62) and ingestion rate (ρ = 0.28) were the leading sources of uncertainty. The region needs to move beyond descriptive monitoring towards a physics-informed and probabilistic style of risk characterisation. To that end, we propose a four-pillar Physics-Informed Environmental Monitoring Framework (PI-EMF) combining multi-compartment sensor networks, computational transport models, isotopic source apportionment, and locally calibrated probabilistic risk assessment. Putting this in place would shift regional environmental management from reactive monitoring to predictive regulation, with measurable public-health gains particularly for children living near mining sites and dumpsites.